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Related Questions
- What are the potential consequences of early stopping on the model's generalizability to new data?
- How does early stopping affect the model's ability to learn from new, unseen data?
- Can early stopping lead to overfitting on the training data, and if so, how does it impact adaptation to new data?
- What are some strategies to balance early stopping with the need for the model to adapt to new data?
- How does the choice of early stopping criteria impact the model's ability to adapt to new data?
- Can early stopping be used in conjunction with other techniques, such as data augmentation or transfer learning, to improve adaptation to new data?
- What are some common pitfalls to avoid when implementing early stopping to ensure the model adapts well to new data?
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